Recommend and validate a budget allocation strategy

Quick Overview

This question evaluates a data scientist's competency in budget allocation, experimental design, KPI definition, segmentation, and causal inference by requiring translation of SQL-derived spend and profitability insights into constrained program- and advertiser-level budget rules while managing concentration risk.

Recommend and validate a budget allocation strategy

Company: Instacart

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

Using insights you would derive from the SQL task (top advertiser by total spend; most profitable program by net profit and by margin), propose a concrete budget allocation for next quarter that maximizes net profit while controlling risk. Constraints and requirements: - Constraints: no program’s budget share may increase by more than 15 percentage points versus current share; total budget is fixed; each advertiser’s spend must include at least one secondary program (≥10% of their budget) to mitigate concentration risk. - Deliverables: (a) recommended program-level budget shares; (b) advertiser-level allocation rules (e.g., by size tier or historical ROI); (c) expected incremental net profit with a back-of-the-envelope calculation and assumptions. - Validation: design an experiment to validate the strategy (choose between geo holdout, randomized advertiser split, or time-based A/B). Specify unit of randomization, sample size/ power considerations, primary KPI (incremental net profit), and guardrails (e.g., CAC, conversion rate, churn, revenue volatility). Define success thresholds and a decision framework. - Analysis plan: segmentation you would run (e.g., advertiser size, vertical, new vs. returning), how you would handle seasonality and regression to the mean, and additional data you would request (e.g., conversion quality, LTV, attribution window). Be explicit about confounders and how you will detect/mitigate them.

Quick Answer: This question evaluates a data scientist's competency in budget allocation, experimental design, KPI definition, segmentation, and causal inference by requiring translation of SQL-derived spend and profitability insights into constrained program- and advertiser-level budget rules while managing concentration risk.

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Oct 13, 2025, 9:49 PM
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Using insights you would derive from the SQL task (top advertiser by total spend; most profitable program by net profit and by margin), propose a concrete budget allocation for next quarter that maximizes net profit while controlling risk. Constraints and requirements:

  • Constraints: no program’s budget share may increase by more than 15 percentage points versus current share; total budget is fixed; each advertiser’s spend must include at least one secondary program (≥10% of their budget) to mitigate concentration risk.
  • Deliverables: (a) recommended program-level budget shares; (b) advertiser-level allocation rules (e.g., by size tier or historical ROI); (c) expected incremental net profit with a back-of-the-envelope calculation and assumptions.
  • Validation: design an experiment to validate the strategy (choose between geo holdout, randomized advertiser split, or time-based A/B). Specify unit of randomization, sample size/ power considerations, primary KPI (incremental net profit), and guardrails (e.g., CAC, conversion rate, churn, revenue volatility). Define success thresholds and a decision framework.
  • Analysis plan: segmentation you would run (e.g., advertiser size, vertical, new vs. returning), how you would handle seasonality and regression to the mean, and additional data you would request (e.g., conversion quality, LTV, attribution window). Be explicit about confounders and how you will detect/mitigate them.
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